Predictive Analytics And Software Testing: How It Enhance Efficiency Application?

Software testing is about being able to determine the risks a software product will be subjected to after going live.

Although there are multiple ways to predict the performance of the solution, none are quite as efficient or promising as predictive analytics are. 

In this post, you will find out how to use innovative technologies for software testing, and the way predictive analytics helps QA specialists optimize the efficiency of functional and performance assessment.

What is Predictive Analytics? 

By definition, predictive analytics is a subset of data science that uses old data sources to forecast unknown events. The prediction process relies on multiple technologies – data mining, machine learning, statistic modeling, artificial intelligence, and many more.

The process of Predictive Analytics

Predictive analytics help businesses make data-driven decisions, manage products and processes proactively, and build efficient forward-looking strategies. 

How Predictive Analytics is Used in Software Testing

The success of software testing depends on how well the QA and software development team can predict product usage patterns and ensure effortless app visitor journeys.

Unfortunately, due to the lack of awareness about the importance of user behavior or data to fuel the decision-making,  testing teams focus on meeting business and functional requirements rather than matching the tool against the expectations of the end-user. 

As a result, QA professionals risk to miss out on errors or inconveniences and release a cumbersome, low-performing app.

Introducing predictive analytics into software testing will offer tech teams a clear perspective on how a user will interact with the solution, what frustrations he might experience, and what the reaction to interface, performance, or functional errors will be.

Predictive analytics has multiple applications in software testing, such as:

  • As a tool for understanding or predicting user behavior
  • Optimizing resource management
  • Streamlining testing activities
  • Predicting possible bug areas

Types of Data Used For Predictive Analytics Models

QA analytics - the data wheel

Tech teams can use predictive analytics to build a data wheel – a range of data types that give a full understanding of how well the product meets functional benchmarks, business goals, and customer expectations. 

Here are the types of data QA teams rely on when adopting predictive analytics in software testing:

  • Defect data – metrics like Defect Density, Defect Distribution, etc. 
  • Testing-related data – information that helps analyze test cases – test coverage, test execution speed, requirements coverage, failure and pass rate, etc. 
  • Development data – helps identify the full coverage of the chosen test strategy. 
  • Application data – all the insights connected to the production environment. 
  • Operation data – the data that covers crash incidents, app monitoring statistics, etc. 
  • Customer usage data – insights on in-app behavior, customer feedback and satisfaction, user journey statistics. 
  • Business metrics help determine how well the product is meeting business goals. 
  • Requirement data is the information that covers the relevance of the requirements, helps keep track of changes. 
Benefits of Predictive Analytics Implementation in QA

As the number of platforms and systems that tech teams need to take into account when releasing a product grows, fully assessing the functionality and performance of the software is becoming increasingly more expensive and time-consuming. 

To achieve the peak testing speed and meet users’ expectations, QA teams start introducing innovative technology into the process of creating plans, designing and executing testing cases.

Predictive analytics, for one, have a vast array of applications in software testing and consulting service, as well as a range of undeniable benefits: 

1. Helps make data-driven decisions

Predictive analytics allows business owners to collect data about prospective software users and transform it into forecasts on expectations, in-app behavior, and possible frustrations. 

By adopting predictive analytics tools and practices, QA professionals can design test cases that match actual user behavior and create a production environment that closely resembles the real-world conditions. 

As a result, when approving the deployment of the final product, project stakeholders can be confident that no user paths or journeys are left untested. 

2. Improved testing efficiency

Predictive analytics can help test teams estimate the amount of time needed to execute different strategies.

By adopting the technology in the planning stage, QA professionals can ensure that the chosen course of action is the most productive, cost-efficient, and fastest to execute. 

3. Gaining competitive advantages over competitors

Recognizing the impact of predictive analytics in software testing helps teams ensure higher operational efficiency, customer satisfaction, and update delivery than that of competitors who don’t rely on data-driven forecasts and make guesswork-based decisions. 

Other than improving the efficiency of testing, collecting and processing more customer data can offer business managers dozens of useful customer relationship insights that can be shared with marketing and sales teams. 

Similarly, missing out on predictive analytics puts the project team at a disadvantage, as opposed to tech-savvy competitors. 

4. Managing risks

Predictive analytics is highly valuable for identifying and dealing with security threats. For instance, a QA team can design a predictive model that alerts project stakeholders as soon as there’s a red flag for an attack or a data breach.

Other than pinpointing potential risks, predictive analytics can help determine the severity of all potential security issues so that teams can prioritize tasks intelligently. 

By adopting predictive models, QA testers can be confident that they are not spending too much time fixing a third-tier issue while the major threat remains unrevealed. 

Predictive Analytics Improve Application Delivery

When it comes to implementing predictive analytics into the software development lifecycle (SDLC), tech teams hope to reduce software engineering and testing costs and meet user expectations with the utmost precision. 

The subset of technologies is the most impactful in three forms:

  • Predictive planning 
  • Predictive QA
  • Predictive DevOps. 

Let’s take a look at how each of these frameworks improves application delivery and cuts development costs. 

1. Predictive planning

Failing to estimate the amount of time, money, and talent needed to deploy the project is one of the most common issues affecting application delivery.

Here’s how predictive analytics can improve the strategizing process and the quality of software consulting service:

  • Determining the amount of time needed for a developer to complete software engineering tasks based on the pace of previous projects
  • Prioritizing all app features by importance to the end-user based on the engagement with the prototype
  • Understanding which features are crucial revenue-drivers for the company. 

2. Predictive DevOps

DevOps as a practice is geared to speed up application delivery. However, the efficiency of the framework can double once predictive analytics is deployed.

For instance, tech teams can use predictive models to determine which coding practices lead to increases and decreases in customer satisfaction.

By collecting data on crashes and app fails, development and testing teams or the R&D center will understand which user actions lead to system shutdowns and will focus on these areas when designing test cases. 

3. Predictive quality assurance

During the testing process, QA teams often struggle to determine which of the code sequences executed by users led to a data leak, a system shutdown, a bug, or a different error.

With predictive analytics, testing professionals will be able to pinpoint the user paths that lead to application failures. 

Other than that, predictive analytics can help identify the fixes to the most common issues based on the data collected during the entire testing cycle.

By knowing the root and the cause of the failure, the similarities between different types of in-app bugs, a QA and development team would know the extent of every error, other areas of the app that are affected by it, and the potential damage a threat can cause. 

Conclusion

Predictive analytics is a highly helpful set of technologies as it improves the quality of decision-making, helps forecast the impact of every change a software testing team makes, and improve the efficiency of quality assurance sessions. 

Tech teams should consider implementing predictive analytics practices as early as possible. Using predictive models for planning and strategizing results in accurate estimates and efficient workflows.

After the product is out, predictive analytics will help prepare for traffic peaks, monitor customer satisfaction fluctuations, and determine high-risk areas that should be updated. 

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